Robust Design for Multiple Quality Characteristics of Precision Product Using Taguchi-based Hybrid Method

2020 ◽  
Author(s):  
Jiawei Wu ◽  
Huaming Song ◽  
Liangqi Wan ◽  
Kashif Abbass ◽  
Taimoor Hassan
Keyword(s):  
Robust Design ◽  
Hybrid Method ◽  
Quality Characteristics ◽  
Multiple Quality Characteristics

Robust Design Based on Grey Correlation Analysis

2013 ◽  
Vol 401-403 ◽  
pp. 272-275
Author(s):  
Lian Xue ◽  
Le Le Dong
Keyword(s):  
System Theory ◽  
Robust Design ◽  
Quality Characteristics ◽  
Grey System Theory ◽  
Grey System ◽  
Grey Correlation ◽  
Multiple Quality Characteristics ◽  
Grey Correlation Analysis ◽  
Best Parameter ◽  
The Stability

In this article, the grey correlation is regarded as the decision index of Multiple Quality Characteristics of the Robust designed by the grey system theory. Then using the Analysis of variance to get the factors of Significantly affecting the stability and the best parameter. At last advantage of the method is proved by examples. It proves that grey system theory in robust design is practical greatly.

scholarly journals Applying surrogate variables to deal with destructive characteristics for robustification using Six Sigma methodology

2015 ◽  
Vol 14 (3-4) ◽  
Author(s):  
Mohammad Amin Javadi ◽  
Sadigh Raissi
Keyword(s):  
Customer Satisfaction ◽  
Robust Design ◽  
Six Sigma ◽  
Quality Characteristics ◽  
Design Concepts ◽  
Multiple Quality Characteristics ◽  
Surrogate Variables ◽  
Six Sigma Methodology ◽  
Noise Factors

<p>Even though there are remarkable advances in machinery and technological methodologies toward products robustness and precise operations on processes, engineers always address difficulties in setting a process at desirable conditions. Actually, due to the nature of the destructive characteristics of products and processes they might not be able to cope with the issue. Aforementioned traits create constraints against direct measurement. Consequently, surrogate variables are proposed to go through the obstacle. Since sources of deviations are easy to affect as noise factors, experts are required to ameliorate skillfully. Multiple quality characteristics are contemplated simultaneously in different industries such as pharmaceutical, food sections or productions for improving both product reliability and customer satisfaction. In order to overcome the expressed problems and facilitate the processes, a model is constructed through robust design concepts and a case study shows how to surmount the problem of working with different destructive quality characteristics at the same time by using Six Sigma methodology.</p>

Mean Square Error Criteria to Multiple Quality Characteristics Robust Design by the Weighted Tchebycheff Method

2010 ◽  
Vol 118-120 ◽  
pp. 881-885 ◽  
Author(s):  
Li Juan Shen ◽  
Yu Zhao ◽  
Jun Yang
Keyword(s):  
Mean Square Error ◽  
Robust Design ◽  
Measurement Model ◽  
Quality Characteristics ◽  
Quality Characteristic ◽  
Quadratic Loss ◽  
Mean Square ◽  
Pareto Solutions ◽  
Multiple Quality Characteristics ◽  
Infinite Norm

Robust design has been widely applied in quality improvement. For most products, quality is multidimensional. Little attention has been paid to multiple quality characteristics robust design (MQCRD). MQCRD mainly faces two problems: measurement model and multiple objectives optimization. In order to give an appropriate measurement criterion and generate better Pareto solutions, a MQCRD model by the Weighted Tchebycheff method based on the mean square error (MSE) criterion is proposed. Firstly the MSE estimations for each quality characteristic are obtained by response surface method (RSM) and a MQCRD model based on the MSE criteria is given. Then a MQCRD model by the Weighted Tchebycheff method is present, which minimizes the distance between objective functions and the idea point based on the weighted infinite norm. The proposed approach could find very efficient Pareto solutions. Illustrative example shows the model can generate more efficient and robust solutions than the weighted-sum approach. Furthermore it has better results than the generalized reduced gradient (GRG) and quadratic loss function approach.

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